Artificial intelligence (AI) and, in particular, machine learning (ML) have become one of the most exciting
and dynamic areas of research in recent years, impacting many domains of science and technology [1]}, [2]}, [3]}, [4]}, [5]}. The main challenge in ML is to devise
algorithms able to recognize patterns in previously unseen data without any explicit instructions by an external party. Among the different existing ML algorithms, artificial neural networks (ANNs) [6]} have currently turned into the most largely ones used in science. Inspired by biological neural networks, the architecture of ANNs consists of an input layer, one or more hidden layers, and an output layer of several interconnected artificial neurons. Combining this architecture with various training algorithms such as back-propagation and stochastic gradient descent, ANNs are able to capture complicated non-linear input-output relationships in datasets. Due to this ability, ANNs can be considered as universal non-linear function approximators [7]}, [8]}, [9]}. Examples of ANNs include among others feed-forward neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, radial basis function networks, Boltzmann machines or restricted Boltzmann machines (see e.g., Ref. [5]} for a detailed description of these and other types of ANNs).
